Maximum Likelihood Quantum Error Mitigation for Algorithms with a Single
Correct Output
- URL: http://arxiv.org/abs/2402.11830v1
- Date: Mon, 19 Feb 2024 04:44:33 GMT
- Title: Maximum Likelihood Quantum Error Mitigation for Algorithms with a Single
Correct Output
- Authors: Dror Baron, Hrushikesh Pramod Patil and Huiyang Zhou
- Abstract summary: Quantum error mitigation is an important technique to reduce the impact of noise in quantum computers.
We propose a simple and effective mitigation scheme, qubit-wise majority vote, for quantum algorithms with a single correct output.
- Score: 5.601537787608725
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum error mitigation is an important technique to reduce the impact of
noise in quantum computers. With more and more qubits being supported on
quantum computers, there are two emerging fundamental challenges. First, the
number of shots required for quantum algorithms with large numbers of qubits
needs to increase in order to obtain a meaningful distribution or expected
value of an observable. Second, although steady progress has been made in
improving the fidelity of each qubit, circuits with a large number of qubits
are likely to produce erroneous results. This low-shot, high-noise regime calls
for highly scalable error mitigation techniques. In this paper, we propose a
simple and effective mitigation scheme, qubit-wise majority vote, for quantum
algorithms with a single correct output. We show that our scheme produces the
maximum likelihood (ML) estimate under certain assumptions, and bound the
number of shots required. Our experimental results on real quantum devices
confirm that our proposed approach requires fewer shots than existing ones, and
can sometimes recover the correct answers even when they are not observed from
the measurement results.
Related papers
- Application of zero-noise extrapolation-based quantum error mitigation to a silicon spin qubit [0.08603957004874943]
We report the implementation of a zero-noise extrapolation-based error mitigation technique on a silicon spin qubit platform.
This technique has been successfully demonstrated for other platforms such as superconducting qubits, trapped-ion qubits, and photonic processors.
arXiv Detail & Related papers (2024-10-14T09:51:21Z) - A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets [1.8419202109872088]
We present a quantum approach to solve a well-studied problem in the context of data sharing.
We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms.
arXiv Detail & Related papers (2024-02-12T20:44:46Z) - Power Characterization of Noisy Quantum Kernels [52.47151453259434]
We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small.
We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
arXiv Detail & Related papers (2024-01-31T01:02:16Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Improving the speed of variational quantum algorithms for quantum error
correction [7.608765913950182]
We consider the problem of devising a suitable Quantum Error Correction (QEC) procedures for a generic quantum noise acting on a quantum circuit.
In general, there is no analytic universal procedure to obtain the encoding and correction unitary gates.
We address this problem using a cost function based on the Quantum Wasserstein distance of order 1.
arXiv Detail & Related papers (2023-01-12T19:44:53Z) - Quantum Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
We study the problem of designing worst-case to average-case reductions for quantum algorithms.
We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
arXiv Detail & Related papers (2022-12-06T22:01:49Z) - Iterative Qubits Management for Quantum Index Searching in a Hybrid
System [56.39703478198019]
IQuCS aims at index searching and counting in a quantum-classical hybrid system.
We implement IQuCS with Qiskit and conduct intensive experiments.
Results demonstrate that it reduces qubits consumption by up to 66.2%.
arXiv Detail & Related papers (2022-09-22T21:54:28Z) - Robust quantum classifier with minimal overhead [0.8057006406834467]
Several quantum algorithms for binary classification based on the kernel method have been proposed.
These algorithms rely on estimating an expectation value, which in turn requires an expensive quantum data encoding procedure to be repeated many times.
We show that the kernel-based binary classification can be performed with a single-qubit measurement regardless of the number and the dimension of the data.
arXiv Detail & Related papers (2021-04-16T14:51:00Z) - Direct Quantum Communications in the Presence of Realistic Noisy
Entanglement [69.25543534545538]
We propose a novel quantum communication scheme relying on realistic noisy pre-shared entanglement.
Our performance analysis shows that the proposed scheme offers competitive QBER, yield, and goodput.
arXiv Detail & Related papers (2020-12-22T13:06:12Z) - As Accurate as Needed, as Efficient as Possible: Approximations in
DD-based Quantum Circuit Simulation [5.119310422637946]
Decision Diagrams (DDs) have previously shown to reduce the required memory in many important cases by exploiting redundancies in the quantum state.
We show that this reduction can be amplified by exploiting the probabilistic nature of quantum computers to achieve even more compact representations.
Specifically, we propose two new DD-based simulation strategies that approximate the quantum states to attain more compact representations.
arXiv Detail & Related papers (2020-12-10T12:02:03Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.